





"""simple docstring"""





# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		Any=0     ):







      '''simple docstring'''




      if name is None:
            lowerCAmelCase    		=			None
      else:
            lowerCAmelCase    		=			""".""" * max(0				,		spaces - 2     ) + """# {:""" + str(50 - spaces     ) + """s}"""
            lowerCAmelCase    		=			fmt.format(SCREAMING_SNAKE_CASE     )

      # Print and recurse (if needed).
      if isinstance(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ):
            if msg is not None:
                  print(SCREAMING_SNAKE_CASE     )
            for k in val.keys():
                  recursive_print(SCREAMING_SNAKE_CASE				,		val[k]				,		spaces + 2     )
      elif isinstance(SCREAMING_SNAKE_CASE				,		torch.Tensor     ):
            print(SCREAMING_SNAKE_CASE				,		""":"""				,		val.size()     )
      else:
            print(SCREAMING_SNAKE_CASE				,		""":"""				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Union[str, Any]				,		SCREAMING_SNAKE_CASE	:		Dict				,		SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      lowerCAmelCase    		=			param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowerCAmelCase    		=			(num_heads, hidden_size, num_splits) + input_shape[1:]
            lowerCAmelCase    		=			param.view(*SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			param.transpose(0				,		2     )
            lowerCAmelCase    		=			param.transpose(1				,		2     ).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowerCAmelCase    		=			(num_heads, num_splits, hidden_size) + input_shape[1:]
            lowerCAmelCase    		=			param.view(*SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			param.transpose(0				,		1     ).contiguous()
      lowerCAmelCase    		=			param.view(*SCREAMING_SNAKE_CASE     )
      return param


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		List[str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			{}

      # old versions did not store training args
      lowerCAmelCase    		=			input_state_dict.get("""args"""				,		SCREAMING_SNAKE_CASE     )
      if ds_args is not None:
            # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
            # from pprint import pprint
            # pprint(vars(ds_args))

            lowerCAmelCase    		=			ds_args.padded_vocab_size
            lowerCAmelCase    		=			ds_args.max_position_embeddings
            lowerCAmelCase    		=			ds_args.hidden_size
            lowerCAmelCase    		=			ds_args.num_layers
            lowerCAmelCase    		=			ds_args.num_attention_heads
            lowerCAmelCase    		=			ds_args.ffn_hidden_size
            # pprint(config)

      # The number of heads.
      lowerCAmelCase    		=			config.n_head
      # The hidden_size per head.
      lowerCAmelCase    		=			config.n_embd // config.n_head
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            lowerCAmelCase    		=			input_state_dict["""checkpoint_version"""]
      else:
            lowerCAmelCase    		=			0.0

      # The model.
      lowerCAmelCase    		=			input_state_dict["""model"""]
      # The language model.
      lowerCAmelCase    		=			model["""language_model"""]
      # The embeddings.
      lowerCAmelCase    		=			lm["""embedding"""]

      # The word embeddings.
      lowerCAmelCase    		=			embeddings["""word_embeddings"""]["""weight"""]
      # Truncate the embedding table to vocab_size rows.
      lowerCAmelCase    		=			word_embeddings[: config.vocab_size, :]
      lowerCAmelCase    		=			word_embeddings

      # The position embeddings.
      lowerCAmelCase    		=			embeddings["""position_embeddings"""]["""weight"""]
      # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
      lowerCAmelCase    		=			pos_embeddings.size(0     )
      if n_positions != config.n_positions:
            raise ValueError(
                F'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'     )
      # Store the position embeddings.
      lowerCAmelCase    		=			pos_embeddings

      # The transformer.
      lowerCAmelCase    		=			lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

      # The regex to extract layer names.
      lowerCAmelCase    		=			re.compile(R"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""     )

      # The simple map of names for "automated" rules.
      lowerCAmelCase    		=			{
          """attention.dense""": """.attn.c_proj.""",
          """self_attention.dense""": """.attn.c_proj.""",
          """mlp.dense_h_to_4h""": """.mlp.c_fc.""",
          """mlp.dense_4h_to_h""": """.mlp.c_proj.""",
      }

      # Extract the layers.
      for key, val in transformer.items():
            # Match the name.
            lowerCAmelCase    		=			layer_re.match(SCREAMING_SNAKE_CASE     )

            # Stop if that's not a layer
            if m is None:
                  break

            # The index of the layer.
            lowerCAmelCase    		=			int(m.group(1     )     )
            # The name of the operation.
            lowerCAmelCase    		=			m.group(2     )
            # Is it a weight or a bias?
            lowerCAmelCase    		=			m.group(3     )

            # The name of the layer.
            lowerCAmelCase    		=			F'transformer.h.{layer_idx}'

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith("""layernorm"""     ):
                  lowerCAmelCase    		=			"""ln_1""" if op_name.startswith("""input"""     ) else """ln_2"""
                  lowerCAmelCase    		=			val

            # Transpose the QKV matrix.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "weight":
                  # Insert a tensor of 1x1xDxD bias.
                  lowerCAmelCase    		=			torch.tril(torch.ones((n_positions, n_positions)				,		dtype=torch.floataa     )     ).view(
                      1				,		1				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
                  lowerCAmelCase    		=			causal_mask

                  # Insert a "dummy" tensor for masked_bias.
                  lowerCAmelCase    		=			torch.tensor(-1e4				,		dtype=torch.floataa     )
                  lowerCAmelCase    		=			masked_bias

                  lowerCAmelCase    		=			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		3				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
                  # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
                  lowerCAmelCase    		=			out_val.transpose(0				,		1     ).contiguous()
                  # Store.
                  lowerCAmelCase    		=			out_val

            # Transpose the bias.
            elif (
                op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
            ) and weight_or_bias == "bias":
                  lowerCAmelCase    		=			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		3				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
                  # Store. No change of shape.
                  lowerCAmelCase    		=			out_val

            # Transpose the weights.
            elif weight_or_bias == "weight":
                  lowerCAmelCase    		=			megatron_to_transformers[op_name]
                  lowerCAmelCase    		=			val.transpose(0				,		1     )

            # Copy the bias.
            elif weight_or_bias == "bias":
                  lowerCAmelCase    		=			megatron_to_transformers[op_name]
                  lowerCAmelCase    		=			val

    # DEBUG.
      assert config.n_layer == layer_idx + 1

      # The final layernorm.
      lowerCAmelCase    		=			transformer["""final_layernorm.weight"""]
      lowerCAmelCase    		=			transformer["""final_layernorm.bias"""]

      # For LM head, transformers' wants the matrix to weight embeddings.
      lowerCAmelCase    		=			word_embeddings

      # It should be done!
      return output_state_dict


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			argparse.ArgumentParser()
      parser.add_argument("""--print-checkpoint-structure"""				,		action="""store_true"""     )
      parser.add_argument(
          """path_to_checkpoint"""				,		type=SCREAMING_SNAKE_CASE				,		help="""Path to the checkpoint file (.zip archive or direct .pt file)"""				,		)
      parser.add_argument(
          """--config_file"""				,		default=""""""				,		type=SCREAMING_SNAKE_CASE				,		help="""An optional config json file describing the pre-trained model."""				,		)
      lowerCAmelCase    		=			parser.parse_args()

      # Extract the basename.
      lowerCAmelCase    		=			os.path.dirname(args.path_to_checkpoint     )

      # Load the model.
      # the .zip is very optional, let's keep it for backward compatibility
      print(F'Extracting PyTorch state dictionary from {args.path_to_checkpoint}'     )
      if args.path_to_checkpoint.endswith(""".zip"""     ):
            with zipfile.ZipFile(args.path_to_checkpoint				,		"""r"""     ) as checkpoint:
                  with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt"""     ) as pytorch_dict:
                        lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      else:
            lowerCAmelCase    		=			torch.load(args.path_to_checkpoint				,		map_location="""cpu"""     )

      lowerCAmelCase    		=			input_state_dict.get("""args"""				,		SCREAMING_SNAKE_CASE     )

      # Read the config, or default to the model released by NVIDIA.
      if args.config_file == "":
            if ds_args is not None:
                  if ds_args.bias_gelu_fusion:
                        lowerCAmelCase    		=			"""gelu_fast"""
                  elif ds_args.openai_gelu:
                        lowerCAmelCase    		=			"""gelu_new"""
                  else:
                        lowerCAmelCase    		=			"""gelu"""
            else:
                  # in the very early days this used to be "gelu_new"
                  lowerCAmelCase    		=			"""gelu_new"""

            # Spell out all parameters in case the defaults change.
            lowerCAmelCase    		=			GPTaConfig(
                vocab_size=5_02_57				,		n_positions=10_24				,		n_embd=10_24				,		n_layer=24				,		n_head=16				,		n_inner=40_96				,		activation_function=SCREAMING_SNAKE_CASE				,		resid_pdrop=0.1				,		embd_pdrop=0.1				,		attn_pdrop=0.1				,		layer_norm_epsilon=1e-5				,		initializer_range=0.02				,		summary_type="""cls_index"""				,		summary_use_proj=SCREAMING_SNAKE_CASE				,		summary_activation=SCREAMING_SNAKE_CASE				,		summary_proj_to_labels=SCREAMING_SNAKE_CASE				,		summary_first_dropout=0.1				,		scale_attn_weights=SCREAMING_SNAKE_CASE				,		use_cache=SCREAMING_SNAKE_CASE				,		bos_token_id=5_02_56				,		eos_token_id=5_02_56				,		)
      else:
            lowerCAmelCase    		=			GPTaConfig.from_json_file(args.config_file     )

      lowerCAmelCase    		=			["""GPT2LMHeadModel"""]

      # Convert.
      print("""Converting"""     )
      lowerCAmelCase    		=			convert_megatron_checkpoint(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # Print the structure of converted state dict.
      if args.print_checkpoint_structure:
            recursive_print(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # Add tokenizer class info to config
      # see https://github.com/huggingface/transformers/issues/13906)
      if ds_args is not None:
            lowerCAmelCase    		=			ds_args.tokenizer_type
            if tokenizer_type == "GPT2BPETokenizer":
                  lowerCAmelCase    		=			"""gpt2"""
            elif tokenizer_type == "PretrainedFromHF":
                  lowerCAmelCase    		=			ds_args.tokenizer_name_or_path
            else:
                  raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}'     )
      else:
            lowerCAmelCase    		=			"""gpt2"""

      lowerCAmelCase    		=			AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			type(SCREAMING_SNAKE_CASE     ).__name__
      lowerCAmelCase    		=			tokenizer_class

      # Store the config to file.
      print("""Saving config"""     )
      config.save_pretrained(SCREAMING_SNAKE_CASE     )

      # Save tokenizer based on args
      print(F'Adding {tokenizer_class} tokenizer files'     )
      tokenizer.save_pretrained(SCREAMING_SNAKE_CASE     )

      # Store the state_dict to file.
      lowerCAmelCase    		=			os.path.join(SCREAMING_SNAKE_CASE				,		"""pytorch_model.bin"""     )
      print(F'Saving checkpoint to "{output_checkpoint_file}"'     )
      torch.save(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


####################################################################################################

if __name__ == "__main__":
       main()

####################################################################################################













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






